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Bridging Technology and Well-being: Applying Machine Learning to Employee Mental Health: An Investigation into the Integration, Impact, and Adoption of Intelligent Solutions
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Orientador(es)
Resumo(s)
This master's thesis explores the application of machine learning techniques to address business-related challenges, with a specific focus on enhancing support for employee mental health. The research aims to fill existing gaps in our understanding of how machine learning can positively impact employee well-being and workplace performance. Utilizing advanced statistical and probabilistic methods, the study will analyze a diverse range of data sources, including surveys and IT company records, to gain insights into various aspects of employee mental health and identify opportunities for improvement. The study will critically evaluate the influence of machine learning tools on employee well-being and performance while also investigating effective strategies for seamlessly integrating these tools into existing workplace wellness programs. Additionally, the thesis will address potential barriers to the adoption and utilization of machine learning tools in supporting employee mental health, offering practical solutions to overcome these challenges. This paper aims to contribute to the development of innovative tools and methodologies that foster a supportive work environment conducive to the improvement of employee mental health, thereby enhancing overall organizational performance and well-being.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
Palavras-chave
Machine Learning Employee Mental Health Data Analysis Workplace Wellness Business-Oriented Issues SDG 3 - Good health and well-being SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 11 - Sustainable cities and communities